Deepfake: A fake news on steroids

Instead of being mostly textual, the fake news era brings digitally
altered video and audio also known as deepfakes. These have a real potential to
further erode already undermined public trust in journalismbut
alsotocause serious security impediments.

The term deepfake was first coined on the online platform Reddit in 2017 by an anonymous user
who called himself ‘deepfakes’. It was coined out of two terms ‘deep learning’
and ‘fake’ and represents a technique for human image synthesis based on
artificial intelligence. It is used to combine and
superimpose existing images and videos onto source images or videos
using a machine learning technique known as Generative Adversarial Network
(GNAs).

The Generative Adversarial Networks was invented in 2014 and
is based on artificial intelligence, which powers the deepfake videos and audios.
In short, the GANs are made up of two rival computer networks, which use the
synthesiser and detector (discriminator) to create the deepfake content.

Experts believe that there can be found about 10,000
deepfake videos circulating on the Internet. The deepfake apps are
proliferating and their use is exponentially rising. For example, the cybersecurity
firm Deeptrace reported that the four leading deepfake-themed
pornography websites, supported by advertising, had attracted 134 million views
for their videos since February 2018. Some reports found that the amount of
deepfake videos circulating online have doubled in less than a year, jumping
from 7,964 in December 2018 to 14,698 this year so far.

There is also evidence that the production of these videos
is becoming a lucrative business. In August, The Wall Street Journal
reported on one of the first known cases of synthetic media becoming
part of a classic identity fraud scheme. The Financial Times
added that scammers are believed to have used commercially available
voice-changing technology to pose as a chief executive in order to swindle
funds.

Moreover, the quality of deepfake videos is rapidly
increasing. “In January 2019, deepfakes were buggy and flickery. Nine months
later, I’ve never seen anything like how fast they’re going. This is the tip of
the iceberg”, said Hany Farid,
a professor at the University of California, Berkeley. Meanwhile, the BBC reported that the audio
deepfakes are also on the rise.

Why do deepfakes flourish?

The success of deepfakes is attributed to its multi-centric (audio
and visual) experiences. Many studies show that we tend to trust broadcast
images, particularly moving images, much more than the written text. It is
proven that videos instantly evoke emotions.

“It’s now become possible to create a passable deepfake
with only a small amount of input material – the algorithms need smaller and
smaller amounts of video or picture footage to train on,” explained Katja
Bego, principal researcher at innovation foundation
Nesta. One such portal required 250 photos of the target subject and
two days of processing to generate a video. The Deeptrace says the prices
charged for deepfakes vary but can be as little as USD 2.99 per video.

While the evidence shows that pornography accounts for the
overwhelming majority of the deepfake clips, the report from Deeptrace highlights the potential for the use of deepfake
technology in political campaigns.

It is well known that disinformation is as old as politics, but
its practitioners have kept pace with technological changes. Where written fake
news was the hallmark of the most recent election cycle in the US and UK,
images and videos are increasingly the new focus of propaganda, says Vidya Narayanan, a researcher at the
Oxford Internet Institute.

The Hollywood movies show that manipulating video is nothing
new. Although it has been possible to alter video footage for decades, it would
take a long time, highly skilled artists, and a lot of money.

However, deepfake technology is rapidly changing the game.
It is now commonly accessible so that almost anybody could make a convincing
fake video. For example, a simple search on the Github platform for the
deepfake free software returns over 100 results, although most of them offer a
modified ‘face swap’ technique. Furthermore, it is easy to find people who are
offering deepfake services for as little as USD 20 per request.

Can ordinary people be a target?

A simple answer is: yes. This is due to the plentifully
available photo, video and audio material on various social media platforms
such as Facebook or Instagram. Hence, basically, anyone can become a potential
target.

Unfortunately, currently, the most imminent threat of
deepfakes comes from weaponizing them against
women. Thus far experience shows that the deepfake makers use women’s faces
without consent and paste it on the pornographic content. This humiliating
tendency is described as ‘revenge porn’. A viral deepfake video can reach an
audience of millions and make headlines within a matter of hours but proving
after the fact that the video was altered might be too little too late.

In any case, it appears that in the short term the real
victims of malicious creators of deepfake videos will not be governments and
corporations but individuals, most of them women. It is unlikely that they will
be able to afford to hire specialists to combat the abusers, believes Rory Cellan-Jones from BBC.

Tom Van de Weghe,
a Stanford University researcher explains that the “deepfake creators only have
to download these pictures and train their models if they want to use it for
identity theft, blackmailing or spreading negative news about anyone – not only
politicians, CEOs or other influential people. This could be used for
information warfare, misleading public opinion, manipulating stock prices or
getting electoral support”.

Yet, it could get worse.
Imagine that only one Facebook profile picture is sufficient to create a
deepfake video. Researchers at the Samsung AI Center in Moscow are already experimenting with this. They recently developed a way to create ‘living
portraits’ from a very small dataset (one picture or portrait) and generated
animations from cultural icons such as Leonardo da Vinci’s Mona Lisa, Albert
Einstein or Marilyn Monroe. This new algorithm goes beyond what other
algorithms, using generative adversarial networks, can accomplish.

The dangers of deepfakes are real indeed. They can be used
to create ‘digital wildfires’. They can be used by any autocratic regime to
discredit dissidents. They can be used to convince people that a dead leader is
still alive. They can generate false statements.

A Japanese start-up Deepfakes Web is
charging USD 2 per hour of processing time to create videos. The Fiverr, an
online marketplace is connecting freelancers with customers, offering to put
customers’ faces into movie clips.

While it gets cheaper to create deepfake videos, the costs
of consequences could be sweeping. Deceitful videos of business leaders could
sink companies while these politicians can instigate a political turmoil even
in fairly stable countries.

The Financial Times cautioned that false
audio of central bankers could swing markets. Small businesses and individuals
could face crippling reputational or financial risk. The news outlet also
warned that, as elections approach in the US, UK and elsewhere, deepfakes could
raise the stakes once more in the electorate’s struggle to know the truth.

The list of examples is almost endless but we can genuinely
call deepfakes fake news on steroids as this dangerous practice can create disbelief
by default: they can question the veracity of real videos in order to undermine
credibility and cast doubt. This can further erode trust in journalism and
create havoc in societies.

Vidya Narayanan
believes that the deepfakes videos and audios may be particularly destructive
in countries such as India or Brazil, in which there is heavy use of the
WhatsApp platform for sharing videos, images and voice messages. The perceived
security of this closed platform can deceive users of these countries with
large populations and without much basic literacy. In such a setting, it is
difficult to give the population basic media literacy.

The Power in
Powerlessness

Unfortunately, there are no commercially available tools to reliably
detect deepfakes as yet. However, it is clear that any technological solution
must involve artificial intelligence as the deepfake creators are quick to catch
up with the latest detection techniques.

Not so reliable indicators

While solutions to countering deepfakes are still far off,
there are some measures that can and should be considered. Some of the indicators people could use to
spot a false video include, for example, observation of the subject’s face between
the person’s chin and nose. In other words, it should be observed if there are blurring
marks, a dropped frame or discolouration in these areas.

Almost motionless eyes, (with a fewer flicker) is also a
sign of deepfakes. The speech out of sync with the movement of the lips is also
a sign of fake video. Observation of an emotional mismatch signals deepfakes as
well as jittering and blurring at unexpected places. The appearance of some strange
objects and analysing the light angels can also point out to the fake videos.

Watching and scrutinising suspicious videos should raise the
questions whether there are any glitches and inconsistencies in the video or
audio, can the footage be corroborated and is the source trustworthy? Of
course, the correctness of these observations depends on the observer’s accurate
training.

In a study from the University of California Berkeley and the
University of Southern California, researchers used machine learning to analyse
politicians’ style of speech and facial and body movement. In the study, detecting
artificial intelligence was accurate 92% of the time in distinguishing a
deepfake.

It, however, seems that major
social media sites are still struggling to restrain the misinformation campaigns.
The Facebook CEO Mark Zuckerberg admitted in June this year that the
company’s systems were too slowin
detecting and removing the false video.

But it also appears that the tech giants are catching up.
While YouTube announced that it was aware of the issue and was working on it,
Google reported that they use deepfakes to fight malicious deepfakes. In the
meantime, Facebook has funded USD 10 million into an effort to spot deepfake
videos. In September, Facebook also announced the launch of the Deepfake
Detection Challenge alongside Microsoft.

The blockchain storage is probably the most promising
approach to restore trust in videos and audios, believes the group of students
at Stanford’s Design School. They have designed a decentralised prototype that
allows tracking origin of digital imagery by providing proof of authenticity. By
design, this model does not include intermediaries or trusted third parties.

Stanford Computer Science
students Cheerla and Suri allow content creators to embed inerasable
digital watermarks in their media using deep neural networks. Even if malicious
attackers modify a video, distort the audio, swap in another person’s face – they
cannot remove the digital signature burned into the content. The creators said
that this signature should always point back to the original, unmodified
content.

However, some researchers believe that the watermarking will
completely change the video. Hence, trying to prove something is a fake without
reference to the original footage would be extremely hard. Watermarking can
also lead to false positives, in other words, the real videos can be flagged as
deepfakes.

As fighting deepfakes and fake news is not only a technological
problem, governments must also play their roles. “As the technology is
advancing so rapidly, it is important for policymakers to now think about
possible responses. This means looking at developing detection tools and
raising public awareness, but also [to] consider the underlying social and
political dynamics that make deepfakes potentially so dangerous”, advises
theDeeptrace report.

China and the U.S recently announced legislative initiatives.
Also, California made it illegal to create or distribute deepfakes in a move,
meant to protect voters from misinformation. It is, however, still not clear
how difficult it might be to enforce such legislation.
On the other hand, there must be exercised
caution about legislation that is targeting deepfakes as these laws and
regulations could be misused against journalists, dissidents or human rights activists.
In that regard, deepfakes should not be treated like other forms of
misinformation or fake news.